Instructions to use Shibyan/qwen-1.8b-test-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Shibyan/qwen-1.8b-test-function-calling with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-1_8B-Chat") model = PeftModel.from_pretrained(base_model, "Shibyan/qwen-1.8b-test-function-calling") - Notebooks
- Google Colab
- Kaggle
train_2024-08-31-17-40-34
This model is a fine-tuned version of qwen/Qwen-1_8B-Chat on the glaive_toolcall_en dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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